Pen-based computing devices (such as pen computers, Tablet PCs and personal digital assistants (PDAs)) are becoming increasingly popular among consumers. Pen-based computing devices utilize an electronic pen (called a stylus) instead of keyboard for input. Pens are used for input because in many situations the computing devices are too small to incorporate a keyboard. In addition, there are numerous situations where a pen together with a notepad is more convenient for the user that a keyboard. These pen-based computing devices generally have special operating systems that support handwriting recognition, which allows a user to interface with the device by writing on a screen or on a tablet instead of typing on a keyboard.
The flourish of pen-based computing devices has created a demand for various handwriting computing techniques. In general, these handwriting computing techniques can be divided into two categories, namely, handwriting recognition and handwriting modulation. Handwriting recognition techniques make it possible for a computer to recognize characters and other symbols written by hand. Most pen-based computing devices incorporate some type of handwriting recognition features. Handwriting modulation includes features like handwriting editing, error correction and script searching.
Handwriting modulation also includes handwriting synthesis. Using handwriting synthesis techniques affords a pen-based computing device several important and useful features. For example, handwriting synthesis techniques can be used to automatically correct a user's writing or spelling errors when using handwriting as input to a pen-based computing device. In addition, handwriting synthesis techniques allow the use of personalized fonts, such as a font of the user's personalized handwriting. Moreover, in many situations people prefer handwriting to typed text because it adds a personal touch. For example, most people prefer to receive and send handwritten personal letters, thank-you notes, greetings, and compliments. Handwriting is used by advertisers to attract the attention of customers. Many people, however, find it easier and more efficient to use a keyboard rather than handwriting. This may be because typing is faster than handwriting or because a person's handwriting is illegible. Thus, in order to personalize the communication yet allow a user to type, it is desirable to be able to convert printed text into the cursive handwriting of the user.
There are a number of approaches that have been used to model and synthesize handwriting. Generally, these approaches can be divided into two categories according to their principles: (a) movement-simulation techniques; and (b) shape-simulation methods. Movement-simulation approaches are physically-based approaches that mimic the pen tip movement of handwriting. Movement-simulation approaches are based on motor models, such as discussed in a paper by R. Plamondon and F. Maarse entitled “An evaluation of motor models of handwriting”, in IEEE Trans. PAMI 19 (5) (1989), pp. 1060-1072. These motor models, which attempt to model the process of handwriting production (or the writing process of the human hand), depend on the dynamic information of handwriting. In a paper by Y. Singer and N. Tishby entitled “Dynamical encoding of cursive handwriting”, in Proc. IEEE Conf. CVPR, 1993 and a paper by H. Chen, O. Agazzi and C. Suen entitled “Piecewise linear modulation model of handwriting”, in Proc. 4th Int'l Conf. Document Analysis and Recognition, Ulm, Germany, 1997, pp. 363-367, modulation models were used to model and represent the velocity of handwriting trajectory. A delta log-normal model was described in a paper by R. Plamondon, A. Alimi, P. Yergeau and F. Leclerc entitled “Modeling velocity profiles of rapid movements: a comparative study”, in Biological Cybernetics, 69 (1993), pp. 119-128. This delta log-normal model was used in a paper by X. Li, M. Parizeau and R. Plamondon entitled “Segmentation and reconstruction of on-line handwriting scripts” in Pattern Recognition 31 (6) (1998), pp. 675-684, for handwritten script compression. Each of these approaches, however, focused on the representation and analysis of real handwriting signals, rather than on the handwriting synthesis. This is because motor models cannot directly be used to synthesize the novel handwriting or user's style, especially in the cursive case.
Shape-simulation approaches only consider the static shape of handwriting trajectory. Shape-simulation approaches, which are based on geometric models, attempt to synthesize curves that are similar to the original handwriting trajectories. These approaches are more practical than movement-simulation techniques when the dynamic information of handwriting samples is not available. In addition, the shape-simulation approaches are more practical when the handwriting trajectory has been re-sampled by other processors (such as recognizers). This is discussed in a paper by H. Beigi entitled “Pre-processing the dynamics of on-line handwriting data, feature extraction and recognition”, in Proc. of the Fifth Int'l Workshop on Frontiers of Handwriting Recognition, Colchester, England, 1996. A straightforward shape-simulation approach is proposed in a paper by l. Guyon entitled “Handwriting synthesis from handwritten glyphs”, in Proc. of the Fifth Int'l Workshop on Frontiers of Handwriting Recognition, Colchester, England, 1996. In this approach by Guyon, handwriting was synthesized from collected handwriting glyphs. Each glyph was a handwriting sample of two or three letters. When synthesizing a long word, this approach simply juxtaposed several glyphs in sequence and did not connect them to generate fluid handwriting. In fact, none of these approaches is capable of synthesizing fluid cursive handwriting in the personal style of a user.
Therefore, there exist a need for a cursive handwriting synthesis system and process capable of mimicking a user's personal handwriting style and reproducing that style such that the synthesized handwriting produced is fluent and natural and an accurate reproduction of the user's personal handwriting style.